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Efficient task scheduling algorithms for heterogeneous multi-cloud environment

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Abstract

Cloud Computing has grown exponentially in the business and research community over the last few years. It is now an emerging field and becomes more popular due to recent advances in virtualization technology. In Cloud Computing, various applications are submitted to the datacenters to obtain some services on pay-per-use basis. However, due to limited resources, some workloads are transferred to other data centers to handle peak client demands. Therefore, scheduling workloads in heterogeneous multi-cloud environment is a hot topic and very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. In this paper, we present three task scheduling algorithms, called MCC, MEMAX and CMMN for heterogeneous multi-cloud environment, which aim to minimize the makespan and maximize the average cloud utilization. The proposed MCC algorithm is a single-phase scheduling whereas rests are two-phase scheduling. We perform rigorous experiments on the proposed algorithms using various benchmark as well as synthetic datasets. Their performances are evaluated in terms of makespan and average cloud utilization and experimental results are compared with that of existing single-phase and two-phase scheduling algorithms to demonstrate the efficacy of the proposed algorithms.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, 768018, India

    Sanjaya K. Panda

  2. Indian School of Mines, Dhanbad, 826004, India

    Prasanta K. Jana

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  1. Sanjaya K. Panda
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  2. Prasanta K. Jana
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Correspondence to Prasanta K. Jana.

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Panda, S.K., Jana, P.K. Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 71, 1505–1533 (2015). https://doi.org/10.1007/s11227-014-1376-6

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